21 to 30 of 50 Results
Jul 21, 2022 - 3D Spatial Data Processing
Shinoto, Maria; Doneus, Michael; Haijima, Hideyuki; Weiser, Hannah; Zahs, Vivien; Kempf, Dominic; Daskalakis, Gwydion; Höfle, Bernhard; Nakamura, Naoko, 2022, "3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard", https://doi.org/10.11588/data/TJNQZG, heiDATA, V2
This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data... |
May 6, 2022 - Institut für Politische Wissenschaft - AG Tosun
Tosun, Jale; Koch, Marcus A., 2022, "Policy mixes for biodiversity: A diffusion analysis of state-level citizens’ initiatives in Germany [Dataset]", https://doi.org/10.11588/data/B19Q25, heiDATA, V1, UNF:6:8qCURosLF1n/pFBwfrZeEg== [fileUNF]
The dataset contains data used to produce Figure 1 in the article. |
Apr 25, 2022 - Biogeochemistry
Schroll, Moritz; Lenhart, Katharina; Greiner, Steffen; Keppler, Frank, 2022, "Making plant methane formation visible – insights from application of 13C-labelled dimethyl sulfoxide [Research Data]", https://doi.org/10.11588/data/VJFIBD, heiDATA, V1, UNF:6:1Pzn25XuesxTt+Ygdeo4vg== [fileUNF]
Methane (CH4) formation by vegetation has been studied intensively over the last 15 years. However, reported CH4 emissions vary by several orders of magnitude, thus making global estimates difficult. Moreover, the mechanism(s) for CH4 formation by plants is (are) largely unknown.... |
Apr 5, 2022 - Biogeochemistry
Schroll, Moritz, 2022, "Methane cycling in a eutrophic lake characterised by multiple stable isotope and flux measurements [data]", https://doi.org/10.11588/data/MU3CON, heiDATA, V1, UNF:6:uMXNN9yPRjWKSRIzJA3uuA== [fileUNF]
Recent estimations demonstrate that methane (CH4) emissions from aquatic systems are responsible for up to half of global CH4 emissions. Lakes represent one of the largest CH4 sources, and emissions are predicted to increase due to global warming. However, there is a large uncert... |
Jan 25, 2022 - 3D Spatial Data Processing
Zahs, Vivien; Winiwarter, Lukas; Anders, Katharina; Williams, Jack G.; Rutzinger, Martin; Bremer, Magnus; Höfle, Bernhard, 2021, "Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code]", https://doi.org/10.11588/data/TGSVUI, heiDATA, V2
The analysis and interpretation of 3D topographic change requires methods that achieve low uncertainties in change quantification. Many recent geoscientific studies that perform point cloud-based topographic change analysis have used the multi-scale-model-to-model-cloudcomparison... |
Jan 7, 2022 - Institut für Politische Wissenschaft - AG Tosun
Tosun, Jale; Rinscheid, Adrian, 2022, "What Drives Engagement in the Clean Energy Ministerial? An Assessment of Domestic-Level Factors [Dataset]", https://doi.org/10.11588/data/PVQVWW, heiDATA, V1, UNF:6:QwjNtOHS1nnVsCIBHgxPMg== [fileUNF]
This study concentrates on the Clean Energy Ministerial (CEM) as one of several high-level global forums on climate change governance. The CEM can be conceptualised as a ‘polycentric’ organisation in which its members collaborate on a wide range of issues concerning the clean ene... |
Dec 9, 2021 - 3D Spatial Data Processing
Anders, Katharina; Winiwarter, Lukas; Höfle, Bernhard, 2021, "Improving change analysis from near-continuous 3D time series by considering full temporal information [Data and Source Code]", https://doi.org/10.11588/data/1L11SQ, heiDATA, V1
This dataset comprises the source code (Python scripts) and data to perform spatiotemporal segmentation in time series of surface change data for a (i) synthetic dataset and (ii) hourly snow cover changes acquired by terrestrial laser scanning. Further details are given in the co... |
Li, Hao; Zech, Johannes; Ludwig, Christina; Fendrich, Sascha; Shapiro, Aurelie; Schultz, Michael; Zipf, Alexander, 2021, "Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]", https://doi.org/10.11588/data/AAKAF9, heiDATA, V1
DATASET FOR JOURNAL PAPER (https://doi.org/10.1016/j.jag.2021.102571) Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning... |
Aug 18, 2021 - 3D Spatial Data Processing
Weiser, Hannah; Winiwarter, Lukas; Anders, Katharina; Fassnacht, Fabian Ewald; Höfle, Bernhard, 2021, "Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code]", https://doi.org/10.11588/data/MZBO7T, heiDATA, V1
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is as a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key... |
Jun 15, 2021 - 3D Spatial Data Processing
Winiwarter, Lukas; Anders, Katharina; Zahs, Vivien; Hämmerle, Martin; Höfle, Bernhard, 2021, "M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]", https://doi.org/10.11588/data/XHYB10, heiDATA, V1
The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the mini... |
